Covid-19 impact on financial growth and guidelines for green recovery in BRICS: fresh insights from econometric analysis

Abstract

Sustainable development and socioeconomic growth are balanced through green economic recovery post pandemic. To statistically examine the coordinated development of green economic growth, foreign direct investment, stock market return and financial development, this paper constructs a complete indicator system for green economic recovery and financial development, by using a VAR model for the BRICS over the period of 2000–2020. Our results demonstrate that FDI significantly improves environmental quality by lowering pollution levels and improve the green economic growth in the region (BRICS). Stock market also has a significant positive effect on green economic growth. On the other side, FDI has a significant detrimental effect on financial development. Finally, financial development has a considerable detrimental impact on environmental deterioration. Our analysis recommends that besides the initiatives in financial growth, FDI and stock market be given priority in order to improve sustainable development.

Introduction

The economies of the BRICS (Brazil, Russia, India, China, and South Africa) and ASEAN areas are significant and dynamic. With 43% of the global population living in BRICS countries, the area has a massive labor force. Openness to trade has long been a hallmark of the ASEAN area, but it has only been since the 1990s that the region has come into its own. As a result, the region’s energy consumption has skyrocketed during the last two decades, increasing by an astounding 65% (Yousaf and Ali 2020). Over the last two to three decades, these nations have seen remarkable transformations, earning them the label of major rising countries with rapid economic expansion. Increases in FDI are also being seen in the BRICS and ASEAN areas (Yin et al. 2021). While ASEAN’s share of FDI inflows to emerging countries was 25% in 2018, the BRICS area garnered 20% of the world’s FDI inflows.

Climate change has become a global issue due to its danger to long-term development (Tang et al. 2018). Increasing CO₂ emissions are responsible for the current problem of climate change. GHG pollution, including Carbon dioxide emission, is one of the factors thought to be contributing to climate change. Countries have been pressured to look for a collaborative response since the adverse impacts of environmental devastation have started to be seen worldwide (Mohsin et al. 2021). Since carbon dioxide emissions pose the greatest danger to global technological advancement and natural resources, research into the factors influencing CO₂ emissions in the atmosphere is essential to achieving low-carbon economic success (Iram et al. 2020). Additionally, the global economy is expanding rapidly, with an increase in production of 3–4% per year, endangering the environment severely due to a rise in the consumption of fossil fuels. Countries throughout the globe are investigating ways to decrease GHG because of the severe climate change brought on by industrialization and the ever-expanding need for energy (Ikram et al. 2019). The productivity of commodities and services is increased through traditional economic growth, which raises a nation’s gross domestic product. However, growing economic activity has adverse environmental effects, such as increased hazardous carbon emissions. Green growth is thus vital for economic growth and fundamental for preserving the environment. However, in addition to aiding in creating a low-carbon economy and sustained green development, green finance, an instrument of stock market development and green economic development, improves the nation’s economic performance (Abbas et al. 2022).

The sustainability of the economic green growth and progress that nations have chosen depends significantly on the availability of natural resources. Environmental issues are essential in determining financial growth and sustainable development, according to empirical research on the relationship between the determinants of economic performance and natural resources. Green innovation helps preserve the association, and environmental management strategies positively impact economic success (Mohsin et al. 2020). In order to achieve ecologically sustainable growth, several nations use the growth and development strategy known as “green growth”. Academics and politicians engaged in a contentious debate concerning the impact of natural resources on the economy’s growth and development during the beginning of the 1990s. (Sun et al. 2019) natural resources are bad for the health of an economy because their richness stunts economic development in comparison with countries with few resources a condition known as the “curse of natural resources”. Natural resources are essential drivers of economic expansion, enhancing economic activity and fostering development. Undoubtedly, various variables and metrics affect the nation’s economic success. Few variables, nevertheless, capture the interest of academics and decision-makers when it comes to economic success. The influence of environmental assets and their availability on the economic performance (Mohsin et al. 2022).

Since the COVID-19 outbreak, policymakers have been more aware of the price volatility of environmental assets and its effects on environmental and macroeconomic factors. Empirical investigations have examined this issue and shown that natural resource market volatility negatively affects a nation’s economic performance (Gareta et al. 2006). Since oil is one of the most traded commodities worldwide, its volatility is the subject of attention. Thus, empirical research found that oil price volatility had a detrimental impact on economic growth. However, several studies have examined the relationship between the volatility of natural resources and economic effectiveness. However, there is a considerable gap in the research on stock market return price volatility and green economic growth. The previous research, in particular, ignored the relationship between natural resource price volatility and economic growth in favor of experimentally examining oil price volatility and its relationship to the stock market (Nhuong and Quang 2022). Consequently, the current study aims to draw attention from academics and policymakers to this developing problem, with empirical findings and consequences for a policy that assists governors in overcoming the volatility of natural resource prices and its link to economic performance. Along with the volatility of natural resource prices, other elements that may impact the nation’s economic performance include oil rents, natural gas rents, and green process innovation. Numerous studies have shown that oil and natural gas rentals significantly contribute to the region’s macroeconomic stability (Bozkurt and Karakilcik 2015).

As far as we know, this is the first study to examine the relationship between financial development, FDI, stock market return and green economic growth in the context of the BRICS nations throughout 2000–2020. This study’s primary contributions are: (Mhadhbi and Terzi 2021) The VAR model employed in this article is a combination of FDI and stock market return and is not a study of the development and modifications of a specific system or the one-way effect mechanism of financial development on green economic recovery. The coupling connection between the system and the economic development system is merged, analyzed statistically, and the conclusions are of significant guiding relevance. (Yarovaya et al. 2020) The singular or unjustified constraint on using conventional research indicators is overcome in this paper. The mechanism analysis is used to determine the complete index of green finance development in various locations. It builds an indicator system out of different aspects: green credit, green insurance, green securities, and carbon finance. It provides an accurate picture of the state of local green finance development and serves as a valuable benchmark for future academic work.

Literature review

Published studies support the link between natural resource price volatility and green economic growth. Academics and policymakers have produced a wealth of literature on stock market development and green economic growth. Most of this research concludes that volatility in natural resource prices has a negative effect on economic growth. There has been extensive research into natural gas and green finance for many countries and geographic areas. This section of the paper contains a comprehensive analysis of the related literature.

Recent literature on energy growth and environment trends has focused on modeling important macroeconomic indexes in ways that consider the moderating effect role of economic structure-economic intricacies in explaining the economic sector’s share of productivity gains. The literature investigates the hypothesis that a country’s economic complexity influences the environment’s reliability. Investigated the connection between ECI and the environment after accounting for the impact of renewable energy consumption, metropolitan areas, and economic development. Lower-middle-income and high-income groups were found to have different impacts on the environment. Their research concluded that FDI reduces environmental quality in the low-income divide.

Consequently, to maintain a clean ecology without compromising for a higher-income aim, lower-income blocs must be mindful of their economic and participating institutions. The impact of ECI on air sustainability is also investigated by (Jena et al. 2021) (measured by CO₂). The investigation results corroborate the EKC phenomena and the beneficial effects of conventional fossil fuels on air sustainability. Intriguingly, the research demonstrates that a rise in China’s FDI reduces CO₂ output, meaning that environmental quality is enhanced. This view of an inverse financial development and environmental correlation is consistent with the conclusion of They used an expanded metric (the ecological footprint) to evaluate the sustainability of the environment in the USA used the data from 1990 to 2016. The relationship between the BRICS nations’ biomass energy usage and stock market return. Financial development in the production function was a part of the analysis. VECM was used to examine the previous relationships between the variables. Over the time period studied, there was significant statistical evidence that both financial growth and capital stock buildup boosted Gross domestic product development in the BRICS the relationship between trade and GDP expansion chimed with the mechanism analysis based on ARDL. The research concluded that biomass energy was a critical factor in BRICS’s sustainable growth from a policy perspective. In addition, (Corbet et al. 2019) investigated the impact of decoupled energy sources on pollution emissions while considering the influence of public corruption scandals. Both the BRICS and the ASEAN economies were analyzed in this research. Using energy with a fossil fuel foundation was shown to have a negative effect on environmental sustainability in both nations. While corruption decreased the sustainability of the environment, the investigation confirmed N-shaped curves for both blocs. Therefore this discovery is consistent with their previous research.

(Bouri et al. 2020) recently investigated the BRICS region, using a carbon-income function to examine the correlations between Gross domestic product, Carbon dioxide emission, and coal rent. Regulative quality’s impact on combating climate change was analyzed. Researching these factors was done using the GMM approach. There was a negative impact on environmental sustainability from GDP expansion. However, pollution emissions were mitigated in the BRICS countries by using renewable energy and reducing carbon damage. (Dutta et al. 2020) used DEA models to investigate the interconnections between BRICS’s sustainable energy, labor, economic productivity, and trade openness. The research found that over the period being analyzed, the contribution to climate change rose due to natural resource and economic expansion. Green finance showed promising signs of improving the BRICS ecosystem during the study period. However, asset development was not yet acceptable for reducing pollution. The research showed that promoting clean energy via clean technology has considerable support. (GU et al. 2020) found that fossil fuels and labor degraded environmental quality, and these results were echoed by (O’Connor et al. 2015) used GMM and OLS regression to uncover the worsening effect of agricultural activity on pollution emission in the BRICS.

Considerable research examining the impact of macroeconomic factors on pollutants in a carbon-income setting medium has yielded conflicting findings due to methodological differences. For the BRICS, there is little literature on the issue that tackles the influence of the Public–private partnership variable on climate change, with the sole exception of the research by (Kyriazis 2019) for China that explored the effect of Public–private partnership in a carbon-income context. Both energy security and green development may be achieved by shifting to more sustainable energy consumption. Energy needs continue to rise, but governments have fewer resources to provide. The Public–private partnership concept was developed to address the twin problems of population expansion and environmental degradation. Long-term partnerships between government agencies and private businesses to provide essential products and services are known as public–private partnerships. One of the most important goals of the SDGs are to increase the efficiency with which energy is produced and used.

Furthermore, sustainable development is often related to the PPP model (Shahzad et al. 2021) Since BRICS is comprised of the world’s largest polluters and energy consumers, the group must adopt policies that protect the environment in the long run. Several studies, based on careful examination of the current literature, have shown evidence that the use of stock market return, green growth, and fossil fuel consumption all contribute to expansion in the economy. This is especially true regarding natural resources and economic performance, where debates may go on for a long time and produce varying conclusions (Hussain Shahzad et al. 2020). There has been a similar rise in the profile of renewable energy as a means of addressing harmful emissions. Not to mention, it promotes ecologically sound economic growth. Since achieving a carbon–neutral and sustainable economy is of paramount importance, green growth is crucial. Subsequently, a body of research investigated many facets of the previous relationship (Du et al. 2010). By using ecologically adapted total factor productivity development and growth of environmentally Linked Innovation as intermediaries for green advancement and environmental advancement, the authors of the current study examined the link between green economic growth, FDI, stock market development and financial development in the BRICS countries, filling a significant gap in the literature. In addition, whereas OLS and GMM were used in earlier research, VAR techniques were used in this study.

Data and methodology

Theoretical framework

Here, we lay forth the theoretical foundation upon which our empirical study rests.

Using the TVP-VAR model, we analyze the interdependence of the return variables by referencing the research of (Le et al. 2021) This strategy expands upon the paradigm established by (Nasreen et al. 2020) The TVP-VAR technique of assessing connectedness has many advantages over prior approaches, the most notable of which is eliminating the need to operate with a fixed window size, which might provide inconsistent findings if left to subjective consideration. Since rolling windows lose no information, appropriate portion sizes would also be feasible. Using the TVP-VAR, we will elaborate on the structure of return connectedness. We used panel VAR models, which permit complete heterogeneity across nations, to investigate the connection between green economic growth, FDI, stock market return, and financial development. For numerous reasons, VAR modeling is a helpful tool for examining this matter. To begin, VAR models may be used to infer the potential dynamical influence between two forms of integration. The framework may provide conclusions about the temporal effects of modifications to one form of connectivity on the other form of integration. Second; VAR models produce data-driven empirical outcomes with few differentiating hypotheses. We also permit wide heterogeneity between nations since the Granger causality test demonstrates that the relationship between two forms of integration might vary somewhat across countries.

For the sake of argument, the following significant form equation describes nation I (I = 1, 2…I).

$$G{\left(L\right)}^i{y}_t^i=d^i+e_t^i$$ 1

where ${G(L)}^i$ it indicates a vector of structure disturbance,$y_t^i$ is m by 1 data vector is m by one constant vector, $e_t^i$ and m is the number of variables involved in the model. It is a matrix polynomial in the lag operator L. Under the hypothesis that these structural perturbations are independent of one another, we get where the diagonal entries represent the variance of these perturbations. All other factors consider those nations vary significantly in terms of ($d^i$).

$$y_t^i=c^i+B{\left(L\right)}^i{y}_{t-1}^i+u_t^i$$ 2

Many methods may be used to revert to the structural form solution from the calculated parameters in the threshold regression equation. As in Sims, Cholesky decomposition of the variance decomposition error terms is used in the under-discussion identification systems to enforce recursion zero limitations on concurrent system parameters. Using VAR to estimate for each nation and then calculate each country’s impulse response. We next compute the mean time series estimate and its standard deviation bands based on the regressors from each nation. Using a Bayesian approach, our quantitative approach is unaffected by non-stationarity (Maghyereh et al. 2016).

Data description

Annual data for the BRICS nations are used, spanning the years 2000 through 2020. (i.e., Brazil, Russia, India, China, and South Africa). Green economic growth (in metric tons), renewable energy consumption (as a percentage of total final energy consumption), Stock market return (as a percentage of GDP), foreign direct investment (as a percentage of GNI), and GDP per capita (in constant 2010 US$) are all taken into consideration. Data for the regulatory quality index come only from the World Bank’s development indicator (World Bank, 2019). Between 2000 and 2014, Brazil produced the most average CO₂ emissions per capita, followed by India (Tables 1, 2, 3, 4, 5, 6, 7, 8, 9).

Table 1.

Descriptive and cross-correlation on green recovery, economic recovery.

Source: Author calculation

	Brazil	China	India	Russia	S.Africa	Green recovery spending	Green as % of GDP
Mean	0	0	0	2.14E−04	2.34E−02	0	0.01
Max	0.41	0.18	0.25	0.49	0.17	9.49	79.04
Min	− 0.65	− 0.13	− 0.31	− 0.33	− 0.15	− 8.84	− 1.38
Std. Dev	0.02	0.02	0.02	0.02	0.01	1.15	7.28
Skewness test	− 3.75	0.61	0.37	5.07	0.9	0.1	1.18
Kurtosis test	280.91	18.65	56.16	139.3	22.66	10.85	10.44
Jarque–Bera	1.27E + 07	40,501	4.64E + 05	3.07E + 06	6.41E + 04	9558	9443
Prob	0	0	0	0	0	0	0
Obs	3945	3945	3945	3945	3945	3945	3945

Table 2.

ADF test for BRICS nations.

Source: Author calculations

Regions/Variables	Variable	ADF test	PP test	Variable	ADF test	PP test	Variable	ADF test
Bahrain	0.08	0.35	0.5	0.19	0.32	− 0.05	0.02	− 0.02
Brazil	0.27	0.15	0.12	0.16	0.19	− 0.07	0.11	0
China	0.33	0.08	0.1	0.15	0.16	− 0.03	0.05	0.04
India	1	0.08	0.13	0.18	0.18	− 0.1	0.09	0.02
Russia				0.12	0.15	− 0.07	0.07	0.01
S.Africa				0.14	0.15	− 0.1	0.11	− 0.04

Table 3.

Statistics results.

Source: Author calculations

	Brazil	China	India	Russia	S
					Africa
Mean	0	0	0	0	0
Maximum	0.17	0.14	0.19	0.24	0.12
Minimum	− 0.19	− 0.13	− 0.15	− 0.26	− 0.14
Std. Dev	0.02	0.02	0.02	0.02	0.02
Skewness	− 0.59	− 0.06	− 0.20	− 0.53	− 0.40
Kurtosis	12.76	10.55	14.12	19.1	7.39
Jarque–Bera	15,877	9374.6	20,336	42,787	3275
Prob	0	0	0	0	0
Obs	3945	3945	3945	3945	3945

Table 4.

The total connection index is referred to as TCI and Volatility data.

Source: Author calculations

	Russia	China	Brazil	India	South Africa	Risk Prices	FDI Prices	From Others
Russia	69.27	4.17	6.18	3.55	9.16	0.88	0.36	30.73
China	4.99	69.36	4.04	7.47	6.92	1.24	0.14	30.64
Brazil	5.61	2.89	68.54	4.71	7.06	1.68	0.45	31.46
India	3.44	6.28	5.15	66.5	6.42	1.4	0.55	33.5
Stock market	8.85	5.74	8.23	6.4	63.97	1.51	0.46	36.03
Green recovery	0.53	0.27	1.41	0.92	1.45	93.2	0.11	6.8
To Others	29.79	24.84	34.44	33.46	36.76	12.13	4.85	437.51
Net	− 0.95	− 5.8	2.99	− 0.04	0.73	5.33	− 3.96	TCI 31.25

Table 5.

List of variables and definitions

Variable	Definition	Source
Green TFP (GTFP)	Green productivity index calculated by the super-efficiency SBM model	WDI
Green finance (GF)	Comprehensive index of green finance	World Bank
Economic development (PGDP)	Logarithmic value of GDP per capita	WDI
Energy structure (ES)	Proportion of coal consumption in total energy consumption	WDI
Urbanization (URB)	Proportion of urban population to total population	WDI
Industrialization (IND)	Ratio of secondary industry to GDP	WDI
Trade openness (TRADE)	Ratio of total trade amount to GDP	IMF
Foreign capital (FDI)	Ratio of actual use of FDI to GDP	IMF
Financial support (FS)	Ratio of financial institutions' loan balances to GDP	WDI
Education (EDU)	Average years of education
R&D expenditure (RD)	Ratio of R&D expenditure to GDP	IMF

Table 6.

The influence of green finance on green total factor productivity

Variable	(1)	(2)	(3)	(4)	(5)
	POLS	RE	FE	Two-way	GMM
GF	0.8900	1.3438⁎⁎⁎	1.6632⁎⁎⁎	1.1682⁎⁎	1.8141⁎⁎⁎
	(2.3237)	(3.5432)	(4.3742)	(2.4481)	(4.5021)
PGDP	1.5369⁎⁎⁎	1.4552⁎⁎⁎	0.6480⁎⁎⁎	0.3077	0.7631⁎⁎⁎
	(13.8990)	(10.6800)	(3.0724)	(0.7033)	(3.1086)
ES	0.0016⁎⁎	0.0011	− 0.0055⁎⁎⁎	− 0.0031⁎	− 0.0043⁎⁎
	(2.1914)	(1.0439)	(− 3.2563)	(− 1.8979)	(− 2.4500)
URB	− 0.0226⁎⁎⁎	− 0.0127	0.0113	− 0.0148	0.0081
	(− 4.3399)	(− 1.3809)	(0.8897)	(− 1.1852)	(0.5791)
IND	− 0.0259⁎⁎⁎	− 0.0403⁎⁎⁎	− 0.0551⁎⁎⁎	− 0.0284⁎⁎⁎	− 0.0583⁎⁎⁎
	(− 5.8696)	(− 8.0666)	(− 9.4760)	(− 4.1349)	(− 9.3698)
TRADE	− 0.0042⁎⁎⁎	− 0.0057⁎⁎⁎	− 0.0010	0.0022	− 0.0020
	(− 4.3583)	(− 3.8519)	(− 0.5003)	(1.0003)	(− 0.8926)
FDI	− 0.0223⁎	− 0.0061	0.0343	0.0245	0.0313
	(− 1.8273)	(− 0.2889)	(1.5043)	(1.1475)	(1.3192)
FS	− 0.0078⁎⁎⁎	− 0.0086⁎⁎⁎	− 0.0067⁎⁎⁎	− 0.0086⁎⁎⁎	− 0.0075⁎⁎⁎
	(− 7.6184)	(− 7.6344)	(− 5.0888)	(− 6.2971)	(− 5.4303)
Green R	− 0.2243⁎⁎⁎	− 0.3299⁎⁎⁎	− 0.1903⁎⁎	− 0.1026	− 0.2102⁎⁎
	(− 4.0208)	(− 4.6914)	(− 2.1975)	(− 0.9625)	(− 2.3650)
R&D	0.3712⁎⁎⁎	0.4165⁎⁎⁎	0.6818⁎⁎⁎	0.7081⁎⁎⁎	0.6923⁎⁎⁎
	(8.7277)	(7.2694)	(6.4685)	(6.6121)	(6.2249)
cons	− 9.6839⁎⁎⁎	− 7.9518⁎⁎⁎	− 2.0513	0.6746	–
	(− 11.6123)	(− 7.7829)	(− 1.5422)	(0.1709)	–
R2	0.5854	0.5254	0.7021	0.8021	0.7145
F/Wald	52.8021⁎⁎⁎	714.212⁎⁎⁎	81.8547⁎⁎⁎	54.5745⁎⁎⁎	69.5521
Obs	390	390	390	390	360

t statistics are in parentheses. ^⁎ p < 0.1, ^⁎⁎ p < 0.05, and ^⁎⁎⁎ p < 0.01

Table 7.

Heterogeneity analysis: green recovery environmental protection

Variable	(1)	(2)	(3)	(4)
	L-GF	H-GF	L-ER	H-ER
GF	1.8991⁎⁎⁎	1.3487⁎⁎	2.0185⁎⁎⁎	1.8803⁎⁎⁎
	(3.5106)	(2.1937)	(3.7431)	(3.3867)
PGDP	0.3514⁎	0.5524	0.4568	0.3161
	(1.6933)	(1.3463)	(1.5465)	(0.8888)
ES	− 0.0088⁎⁎⁎	− 0.0027	− 0.0103⁎⁎	− 0.0026
	(− 4.6343)	(− 1.0165)	(− 2.4799)	(− 1.1301)
URB	0.0412⁎⁎⁎	0.0004	0.0361⁎	0.0186
	(3.0924)	(0.0197)	(1.9696)	(0.8081)
IND	− 0.0358⁎⁎⁎	− 0.0667⁎⁎⁎	− 0.0560⁎⁎⁎	− 0.0552⁎⁎⁎
	(− 6.6758)	(− 5.4604)	(− 6.5129)	(− 5.8539)
TRADE	− 0.0048⁎	0.0006	− 0.0054⁎	0.0075⁎
	(− 1.8721)	(0.2053)	(− 1.9468)	(1.7261)
FDI	− 0.0069	0.0956⁎⁎	0.0218	0.0562
	(− 0.3209)	(2.4282)	(0.7878)	(1.0249)
FS	0.0010	− 0.0085⁎⁎⁎	− 0.0071⁎⁎⁎	− 0.0055⁎⁎
	(0.5253)	(− 4.2529)	(− 3.9097)	(− 2.2551)
EDU	− 0.1978⁎⁎	− 0.0780	− 0.2354⁎⁎	− 0.2038
	(− 2.1033)	(− 0.5861)	(− 2.0776)	(− 1.4724)
R&D	0.2189⁎	1.0504⁎⁎⁎	0.4411⁎⁎⁎	1.0646⁎⁎⁎
	(1.8346)	(5.9587)	(3.1548)	(5.8142)
Cons	− 0.9186	− 1.8455	− 0.4130	0.0218
	(− 0.7169)	(− 0.7033)	(− 0.1981)	(0.0096)
R2	0.8306	0.6388	0.7544	0.6359
F-value	79.4404⁎⁎⁎	28.4712⁎⁎⁎	44.5427⁎⁎⁎	29.6856⁎⁎⁎
Obs	195	195	180	210

t statistics are in parentheses. ^⁎ p < 0.1, ^⁎⁎ p < 0.05, and ^⁎⁎⁎ p < 0.01

Table 8.

Baseline estimation results

Dependent variables	Total green technology innovation				High-quality green technology innovation		Low-quality green technology innovation
	Green patents	Green patents	Green patent ratio	Green patent ratio	Green inventions	Green invention ratio	Green utility	Green utility ratio
	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)
InternetPilot	0.102⁎⁎⁎	0.097⁎⁎⁎	0.835⁎⁎⁎	0.783⁎⁎	0.015⁎	0.463⁎⁎	0.037⁎⁎⁎	0.818⁎⁎⁎
	(0.013)	(0.013)	(0.316)	(0.316)	(0.009)	(0.224)	(0.009)	(0.166)
Lange		0.086⁎⁎⁎		1.208⁎⁎⁎	0.037⁎⁎⁎	0.915⁎⁎⁎	0.029⁎⁎⁎	0.376⁎⁎
		(0.012)		(0.288)	(0.008)	(0.204)	(0.008)	(0.151)
Tobin		− 0.007⁎⁎⁎		− 0.149⁎⁎	− 0.004⁎⁎	− 0.057	− 0.005⁎⁎⁎	− 0.094⁎⁎
		(0.003)		(0.070)	(0.002)	(0.049)	(0.002)	(0.037)
LnSubsidy		0.001		0.005	− 0.003	− 0.002	0.001	− 0.001
		(0.001)		(0.028)	(0.008)	(0.020)	(0.001)	(0.015)
Knesset		0.046⁎⁎⁎		0.596⁎⁎⁎	0.037⁎⁎⁎	0.462⁎⁎⁎	0.022⁎⁎⁎	0.121
		(0.008)		(0.206)	(0.006)	(0.146)	(0.006)	(0.108)
HHI		− 0.152		0.509	− 0.246⁎⁎⁎	− 3.664⁎	0.042	4.955⁎⁎⁎
		(0.114)		(2.786)	(0.081)	(1.973)	(0.080)	(1.466)
Constant	0.257⁎⁎⁎	− 0.869⁎⁎⁎	4.288⁎⁎⁎	− 11.140⁎⁎	− 0.634⁎⁎⁎	− 8.310⁎⁎	− 0.414⁎⁎⁎	− 2.938
	(0.005)	(0.191)	(0.131)	(4.654)	(0.135)	(3.296)	(0.133)	(2.449)
Firm FE	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Year FE	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Industry FE	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
City FE	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Observations	21,623	21,623	21,623	21,623	21,623	21,623	21,623	21,623
R2	0.660	0.663	0.470	0.471	0.653	0.441	0.625	0.435
No. of clusters	281	281	281	281	281	281	281	281

Numbers in parentheses represent robust standard errors. ^⁎⁎⁎, ^⁎⁎, and ^⁎ represent different significance levels, indicating p < 0.01, p < 0.05, and p < 0.1, respectively. The empirical results are clustered at the city level

Table 9.

Time trend test results

Dependent variables	Green Patents	Green Patent Ratio
	(1)	(2)
Treat × Year2008	0.027	0.344
	(0.045)	(0.234)
Treat × Year2009	0.007	0.605
	(0.045)	(0.707)
Treat × Year2010	0.025	0.201
	(0.042)	(0.845)
Treat × Year2011	0.033	0.776
	(0.041)	(0.711)
Treat × Year2012	0.052	0.513
	(0.040)	(0.671)
Treat × Year2013	0.007	0.829
	(0.040)	(0.658)
Treat × Year2015	0.034	0.432
	(0.027)	(0.583)
Treat × Year2016	0.045⁎⁎⁎	0.589⁎⁎⁎
	(0.017)	(0.180)
Treat × Year2017	0.046⁎⁎	0.455⁎⁎⁎
	(0.023)	(0.206)
Treat × Year2018	0.032⁎⁎	0.796⁎⁎
	(0.015)	(0.324)
Constant	− 0.561⁎⁎⁎	− 7.410⁎⁎⁎
	(0.079)	(1.768)
Control variables	Yes	Yes
Firm FE	Yes	Yes
Year FE	Yes	Yes
City FE	Yes	Yes
Industry FE	Yes	Yes
Observations	21,623	21,623
R2	0. 817	0.674

Numbers in parentheses represent robust standard errors. ^⁎⁎⁎, ^⁎⁎, and ^⁎ represent different significance levels, indicating p < 0.01, p < 0.05, and p < 0.1, respectively

Cruces and green recovery techniques

The green recovery and GDP, FDI, and stock markets are all represented in the same way $\left(dt,st\text{and}vt,\right).$ The SVAR model, according to the VAR model, characterizes the orthogonality of these processes as follows: Sub-sample distribution is comprehensively assessed, by which the issue of cross-relationships among sample data and the geographical source of differences is solved successfully, and constraints for the Gini and the Theil index are removed.

$$\text{Green recovery}=G_w+G_{\mathit{nb}}+G_t$$ 3

$$G=\sum _{j=1}^k\sum _{h=1}^k\sum _{i=1}^{n_j}\sum _{r=1}^{n_h}\left|y_{\mathit{ji}}-y_{\mathit{hr}}\right|/2n^2\overline{y}$$ 4

$$G_{\mathit{JJ}}=\sum _{i=1}^{n_j}\sum _{r=1}^{n_j}\left|y_{\mathit{ji}}-y_{\mathit{jr}}\right|/2n^2{\overline{y}}_j$$ 5

$$G_w=\sum _{j=1}^k{G}_{\mathit{JJ}}{p}_j{s}_j$$ 6

$$G_{\mathit{JH}}=\sum _{i=1}^{n_j}\sum _{r=1}^{n_h}\left|y_{\mathit{ji}}-y_{\mathit{hr}}\right|/n_j{n}_h\left({\overline{y}}_j-{\overline{y}}_h\right)$$ 7

$$G_{\mathit{nb}}=\sum _{j=2}^k\sum _{h=1}^{j-1}{G}_{\mathit{jh}}\left(p_j{s}_h+p_h{s}_j\right)D_{\mathit{jh}}$$ 8

$$G_t=\sum _{j=2}^k\sum _{h=1}^{j-1}{G}_{\mathit{jh}}\left(p_j{s}_h+p_h{s}_j\right)(1-D_{\mathit{jh}})$$ 9

Table 2 shows the ADF unit root test. Gini coefficient in Eq. (6) which comprises contributions from intra-regional gap $G_w,$ intra-regional contribution gap $G_{\mathit{nb}},$ and ultra-variable density contribution G_t. Equations (8 and 9) are the formula for calculation for $G_w,G_{\mathit{nb}},$ and G_T, respectively. The indicator of green finance growth level is y_ji (he_r) for any province in the area j (h). Similarly, the aggregate of the green financial development level metrics of 31 provinces in the key regions is indicated by $\overline{y}$. Furthermore, the relative effect of green financial growth indices between j and h area is demarcated by $Pj=nj/n,sj=njyj/ny,\text{and}Djh$$.$

Empirical results and discussion

In reaction to the favorable stock market and FDI prices declines for four quarters, with a 95% likelihood that this negative impact considerably deviates from zero. The impact is little after the China and India after the shock, however. Although modest, the point estimate indicates a favorable reaction beginning in the total connection index after the shock. This finding implies that there is uncertainty over the medium- to long-term impacts of financial and trade integration (Xiuzhen et al. 2022) and (Ullah et al. 2020). Additionally, the risk prices and FDI prices are rather modest in size. FDI and stock market grows by around 14% points in reaction to COVID-19 period, and after five years, it is still above the original level by roughly 6.32% points. The greatest TRADE decline is around 0.520% point. In this two-variable model, economic intermediation has a negative short-term impact on trade integration, but the medium- and long-term impacts are negligible.

The contribution of each variable to the system to better comprehend the influence of various facets of this system Connectedness. Following earlier research by (Reboredo 2015) we divide the world’s nations into two categories based on the median values of their stock market return and imports of basic goods. This makes it possible for us to distinguish between the degree of openness and the dependence on export earnings. Therefore, nations that are open and heavily reliant on primary exports are those that are above the GDP and financial growth median (stock market return > 62.32% high accessibility and renewable energy development > 0.52% high dependence on primary commodities) (less diversified). Likewise, nations with economies that are more diverse and less open are those that fall below the average for GDP and stock market return. By analyzing the varied impacts of the stock market and financial growth on green economic recovery, we re-estimate the bi-variate PVAR model.

Model 3 also shows how GDP might act as a buffer between green economic growth and financial development. The findings show that FDI boosts the beneficial effects of renewable energy production on the green economic recovery in the BRICS since the moderating effect is considerably positive (p = 0.010, p 0.01). This lends credibility to Hypothesis 4. One possible explanation is that fintech amplifies the impact of green financing by making it easier for private companies and individuals to engage in environmentally friendly ventures. On the other hand, fintech has a negative and negligible impact. Considering the contradictory findings in previous studies of the connection between innovation and sustainable effects, our insignificant results lend credence to the complexity of the ecologic impacts of the technology proposed by (Sharif et al. 2020)They explained the environmental and technological consequences at multiple levels.

The endogeneity of the explanatory factors is a possible issue in this research. No direct statistical technique can fully tackle the problem of endogeneity, which specifies whether such a variable correlates with unobserved heterogeneity econometric error factors(Salisu et al. 2022). However, if the endogeneity issue is not dealt with, the regression findings could be skewed, leading to incorrect results. Therefore, to tackle the endogeneity issue, we have used indirect testing. First, we indicated above that while evaluating the research model, we used several dependent variables at the province level (i.e., energy consumption from the previous year, GDP per capita, stock market return, and size of local fiscal spending). There is less potential for endogeneity due to missing data since the control variables are both theoretically and practically relevant. As a second step, we used the instrument variables to do a VAR analysis in two stages (IV). This technique is commonly employed in the econometrics literature due to its perceived superiority over the more traditional generalized method of moments (GMM) approach (Tang et al. 2016). We employ lagging green finance and lagging financial development in each province as control variables for current green economic growth and financial development in BRICS, following the recommendations stated in prior research (Yarovaya et al. 2021). To be considered an instrument variable, the lagged variable at a time (− 1) must (1) be substantially connected to the endogenous variable at a time and (2) not be associated with the error terms at the time. Research using panel data often uses a lagged variable as an “instrument” (Tunio et al. 2021). As can be seen in Table 9, our findings are very consistent with the original regression findings, demonstrating their robustness.

Dependence and spillover with global equity and green markets uncertainty

Covariance and correlations among regional green energy markets

Model 6 demonstrates that, contrary to our predictions, fintech has no appreciable positive influence on the strength of the link between green financing and financial sustainability (a = 0.004, SE = 0.008), thus disproving H5. This is due, in part, to the fact that in China, green finance and economic development are still in their infancy (Rafi et al. 2021). Given the infancy of fintech’s application to green financing, this might explain the minimal moderating impact we observed. Figure 1 demonstrates that innovation significantly facilitates the association between green economic growth and economic structure (α = 0.012, p 0.05), lending credence to FDI.

Fig. 1.

Increased clean energy green product prices

Using last year’s energy usage as an example, we find that models in which financial management and stock market return are dependent variables have a largely beneficial influence on the independent variable while having no appreciable impact on green economic growth. The rationale for this is that although high GHGs emissions in the past may have contributed to financial development, it also had a negative impact on the environment, leading to a negligible effect on the ecological environment of high-quality economic and social development. Gross domestic product per capita has a notable ameliorating impact on environmental quality and economic effectiveness but not on the financial sector. The only substantial correlation between education and productivity in the economy is a negative one. This demonstrates how, as the economy advances, fewer inputs are needed to boost production. The more developed an economy gets, the more efficient its use of resources becomes, and this is shown in its negative correlation with the size of local fiscal outlays.

Table 7 shows considerably positive regression coefficients (r = 0.112) for both the breadth of distribution and the depth of usage of the stock market return. This indicates that the scope and intensity of use greatly encourage long-term economic expansion. No significant impact of digitalization on long-term economic development has been seen. There are at least two potential explanations for this. In the first place, stock market return boosts energy consumption, but increased energy usage may lead to pollution, reducing the human capital development index’s favorable influence on BRICS’ long-term economic prospects Second, there is a progression from surface-level to underlying effects of digital economic advancement on long-term economic growth. Due to a lack of established infrastructure, financial development is not yet used to its full potential in all areas. Another factor slowing the spread of digital technology is the shallow adoption of inclusive digital finance. As a result, inclusive digital finance now fosters long-term economic development by improving both the breadth of coverage and the depth of usage. Empirical findings also suggest that the present level of digitally equitable financial inclusion is more effective than the usage level in supporting long-term economic development.

Control variables in a regression analysis show that technological advancement has a very beneficial effect on global energy usage. However, the benefits of reducing fossil fuel use are less clear. BRICS skewed economic structure has resulted in a massive need for fossil fuels to power its fast industrialization and urbanization. Energy consumption structural improvements brought about by technological advancement are not immediately visible against the present backdrop of growing urbanization. The percentage of secondary industries and overall energy usage are positively correlated. The demand for fossil fuels is primarily responsible for the upward trend in overall energy consumption. The share of the tertiary sector economy and the use of fossil fuels are positively correlated. However, the effect of the tertiary industry’s share of the economy on fossil fuel consumption is negligible when compared to the share of the secondary sector. The severity of environmental regulation may effectively reduce the use of fossil fuels and has a significant beneficial influence on overall energy consumption.

Concluding and policy implication

This study examined the relationship between green financing, financial development, and stock market return on green economic growth in BRICS nations to set the guidelines after pandemic. This study’s findings demonstrated that pollution caused by emissions of gases like CO₂ and GHGs raises the cost of public health services. Since of this, the economy will suffer because no outside investors will be interested in funding this initiative. When it comes to green economic growth, using renewable energy sources is a win–win since it helps keep the planet healthy while bolstering long-term growth and development. Considering the importance of sustainable development to long-term economic growth, this bodes well for the economy’s future and should attract more investment from the outside. The findings of this study support the idea that environmental variables play a significant role in the beneficial relationship between green financing and economic development.

Policy implications

To assist financial inclusion, FDI and stock market return better support green economic growth, we offer the following policy suggestions.

We must first change our current financial growth, encourage the merging of conventional banking with stock market return, and fortify the latter’s backing of green economic growth. Banks and other conventional financial institutions need to get with the program and embrace digital finance and optimize their use of financial development to keep up with market demands.

To encourage more investment in technological innovation among industrial and private businesses, the government should, secondly, create distinct innovation incentive schemes. Because of these results, the government should prioritize green innovation by establishing policies like environmental investment, incentive tax policies, and subsidies for manufacturers and private businesses that make use of green finance to stimulate green economic growth. At the same time, the administration should work to reduce systemic risk in the financing of new organizations by focusing on promoting international alternative funding operations for industrial production and private industries through measures like government-led equity investment.

Third, there should be more reforms to the environmental decentralization system, and central environmental regulatory oversight should be enhanced. The results demonstrate that areas with more environmental decentralization have access to digital financial resources better suited to promoting the invention of green technologies. To encourage more initiative from subnational entities in global management, the federal government should provide them more authority over environmental management resources including staff deployment and financial allocation.

This research is a welcome addition to the literature since it studies the moderating influence of environmental decentralization in the connection between FDI, GDP, stock market return and green economic growth. The study has several gaps, however, that will hopefully be filled by further studies. In conclusion, this article explores the notion of environmental decentralization, which includes not only the decentralization of protecting the environment between the federal and local governments, but also the decentralization of environmental conservation agencies within those governments. Green economic growth efficiency in green finance is enhanced by environmental decentralization from above, but whether horizontally environmentally decentralization has a similar impact remains to be addressed.